DataBloom

I’m trying to train a tensorflow keras sequential model with multiple layers, save it, and reload it for testing. But the accuracy is never preserved over saving/loading. I saw that this was a bug that was supposedly fixed in best tensorflow versions, but I have the most recent tensorflow update with no luck. Does anyone else have this problem and any idea of how to fix? Even my CS professor told me to give up and use pytorch instead 😭

I’m helping my daughter setup a camera that will identify birds that show up to her bird feeders using this (https://tfhub.dev/google/aiy/vision/classifier/birds_V1/1) model. I’ve got it working (haven’t started running it with the camera yet, but I already figured out how to get the frames off of that…) BUT, it seems to have a LOT of trouble making an identification when there are multiple birds in the shot. For example, a picture of an empty birdfeeder came back as chickadee at ~20%, but a picture with 8 sparrows came back as sparrow but only at ~15%… similarly, a very clear shot of a cardinal with 5 other birds came back as cardinal but only at 21%… I do understand that the model isn’t going to be perfectly accurate and 20% means the model isn’t very confident (I’m not concerned about that…), but I need to set some bottom thresh-hold and I’m concerned this means any time there are multiple birds at the feeders the system will basically stop working which would end up being most of the time…

So do i need to run an object detection model on the images and clip out individual images of “birds” and then run this identification model? and if so, would anyone have a suggestion on an easyish way to do this? I’m far from a competent coder so advice/suggestions are welcome!

Hi all,

I’ve a TF1.X code with the tf.contrib.layers.batch_norm layer, and Im not sure how to replace it, is it with the tf.compat.v1.layers.batch_normalization layer? Certain variables, such as data_format and scope, are no longer present, so Im not sure it is the correct replacement.

I’ve been meaning to learn how to write machine learning programs in python. As keen as I am, I haven’t found an easy project to get me started.

Any suggestions?

I’m training a deep and wide model with a convolutional side which I’m using inception blocks for. I needed to put in some Batch Normalization layers to stop exploding gradients, and I get a ValueError that points to the BatchNormalization layer creating multiple variables. I can’t find anyone else with this problem, so I don’t know what is causing it. I found that if I set it to eager mode, the error doesn’t come up during training, but will prevent me from saving my model. Any ideas on what is causing this?

preface – My 7yo daughter wants to setup a camera that tells her when birds land at her bird feeders so I’m trying to help her (and take the opportunity to expose her to software/code and apparently ML)… I am in no way a software engineer or coder, so please excuse any complete ignorance…

I’m trying to implement this model (with python 3.10). I’ve found several tutorials that seem straight forward enough BUT… when I download and decompile the project (pulling it off of TFhub was causing issues so I figured this would be a simpler starting point) it doesn’t have a labels file, which all the info I’ve found seems to require… I did find an excel doc linked within the description, but it is just two columns (id and name). do I need to load this as the labels file? and how should I do that (dict?)? I’m assuming the the TF model will output an array of IDs with probabilities it’s correct? and I can then convert those IDs to a “name” however I want? or is the labels file critical to the model functioning?

Thanks in advance for any help.

I have no formal education here but my understanding is that RNNs take an input window and “unfold it”, basing each prediction in part on those prior. Say I have a batch size of 1: There shouldn’t be a relationship between the first batch and second, correct? (if not, tell me; the rest is irrelevant)

Does it follow from my understanding that it’s safe to

• Have overlapping windows in my data? (so conceptually, have batches 0, 1, 2 = data[0:4, 1:5, 2: 6])
• Split into fit/val sets derived from random choices of windows? (rather than just slicing twice)
• Shuffle data after windowing?